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Smartphones are the most common wearable devices. In every single smartphone, more than 20 sensors continuously track all kind of behavioral and environmental data, from geo-location to even remote influences like magnetic field and local gravitation.

To develop meaningful models of users’ behavior, we need to put the technical measurements into context. This is done via interactions with an app. We at Datarella trigger the interactions on the phone by geo-fencing (coming near a pre-defined location, e.g. a retailer’s outlet), and other “complex events” using an Event Processing Language (EPL) and a Complex Event Processing Engine (CEPE).

We want also show best practices how to deal with privacy and informational self-determination, and how to give users a fair deal and transparancy to the data, that we collect by their cooperation in our projects.

3.
Mobile
3
• Two billion people use smartphones (three times more than users of PCs)
• Smartphones are far more than just „mobile computers“: they carry more than twenty sensors or
probes, continuously monitoring our behavior and our environment.
• Mobile is becoming the most important source of human generated data and surpasses social
networks.
• Apart from people using their phones, there are more than five billion mobile devices, connected to
objects, like e.g. cars. These build the Internet of Things.

8.
Our App: explore
• We started our own app 'explore':
• explore tracks all kinds of sensor data on the
smartphone. The data can be collected for
analysis, and it can trigger interactions (like
asking questions or offering suggestions).
• The open beta is available on Google Play
Store; the iOS version should be ready by
J uly 2014.
8 Datarella - Joerg Blumtritt

11.
• Not all data is telling streightforeward like geolocation.
Gyroscope data e.g. is measured in three dimensions.
• This plot shows typical artefacts: the spikes shooting
out of the clutter in regular intervals. These are
caused by hardware inaccuracies, or also by aliasing
effects.
• The artefacts are unique to each device, like a
fingerprint, and can identify the source of the data.
11

13.
Events
• To see what happens, we have to process the
data. How people move arround is visible through
the gyroscope - you see the turns, changes in
directions ect.
• With gyroscopic data in combination with
acceleration and speed, also the means of
transportation can be revealed: walking has a
distinct signature, driving by car shows more
changes in directions then sitting on a train, etc.
• However: the data is noisy; artefacts emerge from
different brands of the sensors, of glitches in the
operating systems, and also can be caused by
environmental influences.
• Take e.g. the rhytmik spikes in the picture below:
nobody would turn rhythmically and so fast.
• So we have to preprocess the data in the app, to
really see, what happens.
13 Datarella - Joerg Blumtritt

14.
What is behavior?
• The normalized gyroscopic data on the right
shows the movements of a person going
from her desk into the kitchen, fixing a pot of
tea, leaving the kitchen and returning to her
desk.
• Sampling rate was 10s, timeframe is 15min.
• We notice episodes of different behavior:
• turning sharply
• walking
• turning smoothly
• walking again
• entering the kitchen, preparing the pot
• waiting for the water to boil
• standing up, leaving the kitchen
• sitting down again
14
0,0
1,0
2,0
3,0
4,0
5,0
6,0
7,0
8,0
9,0
1 4 7 1013161922252831343740434649525558616467707376798285
Datarella - Joerg Blumtritt

15.
Complex Event Processing
• Simple events, like changing direction,
entering an area of specific geo-
coordinates, or having moved for a
specific time span can be combined to
complex events.
• EPL (event processing language)
offeres a way to listen to the data
stream and detect the occurance of
events.
• EPL looks like SQL, but instead of
tables, the search goes into the data
stream.
• For our app, we define events, boolean-
combine these events in a GUI and
parse the definition in the app via J SON
doc.
• The event processing itself takes place
in the app - no network connection is
needed.
15
SELECT ID AS sensorId
FROM ExampleStream
RETAIN 60 SECONDS
WHERE Observation= '' Outlet"
Datarella - Joerg Blumtritt

17.
Travel
• Studying the means of transportation, they
paths, people choose for their communte or
travel, is a streightforward application of our
data.
• We work e.g. for airports to optimize the
shops they would offer to passengers. Since
many passengers come from other cultures,
it is not an easy task for an airport (or in
general for a shopping mall) to learn the
preferences of potential clients - not
consitent shopping data or market research
is available.
• So, e.g. we incentivize passengers from
China to let us accompany their stay in
Euorpe with our app 'explore'. So we can
understand, what they wanted to buy, if they
succeded and if they would have missed
anything, that an airport could have offered
to them.
17 Datarella - Joerg Blumtritt

21.
Quantified Self
• explore offers clear and simple analytics of
the data collected. People can also get their
raw-data for their own purposes.
• We want people to be aware what we (and
other apps) do on the phone. So we do not
only tell in advance, we also show what
sensors are activated and give the opportunity
to opt-out per sensor.
• Since we reflect the results of our tracking as
well as questionnairs and interactions in form
of diagrams and sumaries, we hope, people
will realize what we are doing and can act
self-determined.
• Of couse we respect take-down notices: if
people ask for their data to be deleted, we
follow their request (which btw is also
required by German data protection laws);
this is also a reason for us not to use common
cloud storage and cloud computing platforms,
since we would not have control over the
back-up.
Datarella - Joerg Blumtritt21

22.
22
• Use case: Tracking Chinese
Passengers.
• An international airport wants to learn,
what Chinese passengers buy, what
they consider a pleasent shopping
experience, and what they expect.
• We recruited a panel of Chinese
passengers before they left from
China to Europe, and accompanied
them with our app.
• We learned where they went and
could ask them about their
experiences.

23.
23
• Use case: Driver Timeline
• We built an app that tracks the driving and computes more abstract events from its data,
like „stuck in a traffic jam“.
• The events are displayed in form of a timeline, and can be shared to others.

24.
Battery
• Battery data is both interesting in itself,
and also important to maintain the app
usable.
• Battery consumptions is telling a lot
about the environment of the phone:
temperature, moisture, even air pressure
can be derived using the change in
charge.
24 Datarella - Joerg Blumtritt

25.
Identify whereabouts by
location-specific magnetic field
• Every place has a distinct
signature of the magnetic field
(in strenght like shown on my
own tracking data on the right
as well as in bearing).
• So even if someone decided
to not-track geolocation, we
might still get sufficient
information on their
whereabouts via other
measurements.
• That this is not hypothetical
can be seen on the diagram:
the field's signature of my
home is different from other
places, I stayed during that
week.
25 Datarella - Joerg Blumtritt

26.
26
• We try to help people understand what
their phone does:
• First, in our apps, we provide info on
which probes are active and give the
choice to switch a single probe off to
the user.
• Second, we also feedback
visualizations on the data readings to
the users in our apps (next slide).
• Third, we give users access to their
data via web api.

29.
Spooky Wifi
Self-Tracking vs. Others-Tracking
• You can't avoid tracking others
involunarily, too. This is a problem.
People might be aware, what they
themselves are doing. But others
might be tracked along without giving
their consent.
• Wifi is a good example of "others-
tracking": all wifi signals within reach
are tracked by the phone. It tells a lot
about other people; not only about the
devices they use.
29 Datarella - Joerg Blumtritt

30.
Postprivacy, and communalization of
private life
• In Neal Stephenson's "Snow Crash", we read about the 'Central
Intelligence Corporation' - a commercialized version of today's
NSA. Mobile health, computational social science, and mass
measurement of environmental influences are obvious and benign
applications of QS for the public good. With quantifying and
making public, what European data protection law defines as "the
most intimate personal data", however do we transform the
current "knowledge-database" character of the Net along with its
communication-networks to something new, something that might
become similar to Stephenson's vision?
• Could this even lead to Teilhard's (resp. McLuhan's) angelization
of humans, not only connected via social media but bodily knit
into the data? Would we rather end up in a rally bucolic global
village with moral control by the panoptic community (and an
inherent abelism that comes with a village life)? In both aspects,
representative aggregates like society as well as the concept of
the individual might be rendered obsolete.
30 Datarella - Joerg Blumtritt

33.
Becoming cyborgs?
• The bodily extension into the data
squere - this is what cyborgism is
really about.
• People like Neal Harbison or Enno
Park are pushing the discussion in
that direction: How do we maintain
posession of our bodies? What ethic
framework has there to be set-up?
How do we avoid technological
extensions becoming "black boxes"
that control us, rather than we do
them?
• So it is worthwhile to follow the
proceedings of the Cyborg e.V that
Enno founded.
33 Datarella - Joerg Blumtritt